Pub Date : 2022-03-22DOI: 10.3389/fddsv.2022.858006
M. Yan, Ming Yang, Ching‐lung Lai
Since the COVID-19 outbreak in China in 2019, the pandemic has spread globally. There is no definitive cure, but vaccines have greatly protected humans from symptomatic infections and severe complications. However, vaccine efficacy has been greatly reduced by the advent of SARS-CoV-2 variants worldwide. The World Health Organization has classified the variants into two groups: variants of concern (Alpha, Beta, Gamma, Delta, Omicron) and variants of interest (Lambda, Mu). Clinical trials and modifications of vaccines are currently undertaken to improve their clinical efficacies. This is particularly worrying in immunocompromised patients since breakthrough infections with multiple lineages of variants can pose a continuous threat of severe diseases in these vulnerable subjects, though there is no evidence showing immunocompromised patients are at a higher risk of vaccine-associated adverse events. However, there is no consensus on the schedule, benefits, and risks as well as contraindications (both absolute and relative) of receiving booster vaccinations. This review looks into the efficacy and safety of COVID-19 vaccination booster to guide clinical decisions on when and who to receive booster vaccination.
{"title":"Review of Clinical Trials of COVID-19 Vaccination Booster in SARS-CoV-2 Variants Era: To Take It or Not To Take It","authors":"M. Yan, Ming Yang, Ching‐lung Lai","doi":"10.3389/fddsv.2022.858006","DOIUrl":"https://doi.org/10.3389/fddsv.2022.858006","url":null,"abstract":"Since the COVID-19 outbreak in China in 2019, the pandemic has spread globally. There is no definitive cure, but vaccines have greatly protected humans from symptomatic infections and severe complications. However, vaccine efficacy has been greatly reduced by the advent of SARS-CoV-2 variants worldwide. The World Health Organization has classified the variants into two groups: variants of concern (Alpha, Beta, Gamma, Delta, Omicron) and variants of interest (Lambda, Mu). Clinical trials and modifications of vaccines are currently undertaken to improve their clinical efficacies. This is particularly worrying in immunocompromised patients since breakthrough infections with multiple lineages of variants can pose a continuous threat of severe diseases in these vulnerable subjects, though there is no evidence showing immunocompromised patients are at a higher risk of vaccine-associated adverse events. However, there is no consensus on the schedule, benefits, and risks as well as contraindications (both absolute and relative) of receiving booster vaccinations. This review looks into the efficacy and safety of COVID-19 vaccination booster to guide clinical decisions on when and who to receive booster vaccination.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45217968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-10DOI: 10.3389/fddsv.2022.815017
M. Naziri, Arezoo Ghafari, Hoda Mehrabi, Elham Ramezannezhad, Farzaneh Nazari, Arina Ansari, Farhad Nikzad, N. Deravi
Cancer is among the most life-threatening diseases worldwide. Along with conventional therapies like chemotherapy, surgery, and radiotherapy, alternative treatment approaches such as traditional Chinese medicine have attracted considerable public and scientific interest that could be beneficial for patients diagnosed with cancer. Salvia miltiorrhiza Bunge is greatly beloved for its roots and is extensively applied for various disease therapies, including cancers in traditional Chinese medicine. In this review, we intend to summarize the anti-cancer properties of Cryptotanshinone (CPT), an extract of Danshen (the root of Salvia miltiorrhiza Bunge), on different types of cancer.
{"title":"A Mini-Review of the Anticancer Properties of Cryptotanshinone: A Quinoid Diterpene Extracted From the Root of Salvia miotiorrhiza Bunge","authors":"M. Naziri, Arezoo Ghafari, Hoda Mehrabi, Elham Ramezannezhad, Farzaneh Nazari, Arina Ansari, Farhad Nikzad, N. Deravi","doi":"10.3389/fddsv.2022.815017","DOIUrl":"https://doi.org/10.3389/fddsv.2022.815017","url":null,"abstract":"Cancer is among the most life-threatening diseases worldwide. Along with conventional therapies like chemotherapy, surgery, and radiotherapy, alternative treatment approaches such as traditional Chinese medicine have attracted considerable public and scientific interest that could be beneficial for patients diagnosed with cancer. Salvia miltiorrhiza Bunge is greatly beloved for its roots and is extensively applied for various disease therapies, including cancers in traditional Chinese medicine. In this review, we intend to summarize the anti-cancer properties of Cryptotanshinone (CPT), an extract of Danshen (the root of Salvia miltiorrhiza Bunge), on different types of cancer.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91368838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-07DOI: 10.3389/fddsv.2022.829043
J. Bajorath
In recent years, deep learning (DL) has led to new scientific developments with immediate implications for computer-aided drug design (CADD). These include advances in both small molecular and macromolecular modeling, as highlighted herein. Going forward, these developments also challenge CADD in different ways and require further progress to fully realize their potential for drug discovery. For CADD, these are exciting times and at the very least, the dynamics of the discipline will further increase.
{"title":"Deep Machine Learning for Computer-Aided Drug Design","authors":"J. Bajorath","doi":"10.3389/fddsv.2022.829043","DOIUrl":"https://doi.org/10.3389/fddsv.2022.829043","url":null,"abstract":"In recent years, deep learning (DL) has led to new scientific developments with immediate implications for computer-aided drug design (CADD). These include advances in both small molecular and macromolecular modeling, as highlighted herein. Going forward, these developments also challenge CADD in different ways and require further progress to fully realize their potential for drug discovery. For CADD, these are exciting times and at the very least, the dynamics of the discipline will further increase.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49241596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.3389/fddsv.2022.1027401
Charles C Hong
Heart disease is the #1 killer worldwide, greater than all cancers combined. This is despite the fact that, in the developed world, there has been a substantial decline in cardiovascular mortality since the mid-20th century (Centers for Disease Control and Prevention (CDC), 1999), driven largely by a reduction in ischemic heart disease (Mensah et al., 2017; Nowbar et al., 2019). This decline is multifactorial, involving a reduction in tobacco use, changes in diet, treatment of hypertension, advances in rapid coronary revascularization, and the advent of β-hydroxy β-methylglutaryl-CoA (HMG-CoA) reductase inhibitors, and P2Y12 ADP receptor antagonists (Arnett et al., 2019). However, with the adoption of the Western diet and lifestyle in the developing world, and the rise in prevalence of cardiometabolic diseases and obesity, there has been an increase in the global burden of cardiovascular diseases (CVD) (Roth et al., 2020) and a stalling of improvements in the United States (Sinatra and Huston, 2020).
{"title":"The grand challenge of discovering new cardiovascular drugs.","authors":"Charles C Hong","doi":"10.3389/fddsv.2022.1027401","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1027401","url":null,"abstract":"Heart disease is the #1 killer worldwide, greater than all cancers combined. This is despite the fact that, in the developed world, there has been a substantial decline in cardiovascular mortality since the mid-20th century (Centers for Disease Control and Prevention (CDC), 1999), driven largely by a reduction in ischemic heart disease (Mensah et al., 2017; Nowbar et al., 2019). This decline is multifactorial, involving a reduction in tobacco use, changes in diet, treatment of hypertension, advances in rapid coronary revascularization, and the advent of β-hydroxy β-methylglutaryl-CoA (HMG-CoA) reductase inhibitors, and P2Y12 ADP receptor antagonists (Arnett et al., 2019). However, with the adoption of the Western diet and lifestyle in the developing world, and the rise in prevalence of cardiometabolic diseases and obesity, there has been an increase in the global burden of cardiovascular diseases (CVD) (Roth et al., 2020) and a stalling of improvements in the United States (Sinatra and Huston, 2020).","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"2 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9404673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.3389/fddsv.2022.952326
Aimee E Mattei, Andres H Gutierrez, William D Martin, Frances E Terry, Brian J Roberts, Amy S Rosenberg, Anne S De Groot
The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and "correct" in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.
{"title":"<i>In silico</i> Immunogenicity Assessment for Sequences Containing Unnatural Amino Acids: A Method Using Existing <i>in silico</i> Algorithm Infrastructure and a Vision for Future Enhancements.","authors":"Aimee E Mattei, Andres H Gutierrez, William D Martin, Frances E Terry, Brian J Roberts, Amy S Rosenberg, Anne S De Groot","doi":"10.3389/fddsv.2022.952326","DOIUrl":"https://doi.org/10.3389/fddsv.2022.952326","url":null,"abstract":"<p><p>The <i>in silico</i> prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an <i>in silico</i> method for modeling the impact of a given UAA on a peptide's likelihood of binding to HLA and, by extension, its immunogenic potential. <i>In silico</i> assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory <i>in vitro, ex vivo</i> and <i>in vivo</i> assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating <i>in silico</i> immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and \"correct\" <i>in silico</i> estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.</p>","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"2 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10026553/pdf/nihms-1854666.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9181785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.3389/fddsv.2021.818003
Christopher R. Apostol, K. Bernard, Parthasaradhireddy Tanguturi, G. Molnar, M. J. Bartlett, L. Szábo, Chenxi Liu, J. B. Ortiz, Maha Saber, K. Giordano, T. Green, James Melvin, Helena W. Morrison, L. Madhavan, R. Rowe, J. Streicher, M. Heien, T. Falk, R. Polt
There is an unmet clinical need for curative therapies to treat neurodegenerative disorders. Most mainstay treatments currently on the market only alleviate specific symptoms and do not reverse disease progression. The Pituitary adenylate cyclase-activating polypeptide (PACAP), an endogenous neuropeptide hormone, has been extensively studied as a potential regenerative therapeutic. PACAP is widely distributed in the central nervous system (CNS) and exerts its neuroprotective and neurotrophic effects via the related Class B GPCRs PAC1, VPAC1, and VPAC2, at which the hormone shows roughly equal activity. Vasoactive intestinal peptide (VIP) also activates these receptors, and this close analogue of PACAP has also shown to promote neuronal survival in various animal models of acute and progressive neurodegenerative diseases. However, PACAP’s poor pharmacokinetic profile (non-linear PK/PD), and more importantly its limited blood-brain barrier (BBB) permeability has hampered development of this peptide as a therapeutic. We have demonstrated that glycosylation of PACAP and related peptides promotes penetration of the BBB and improves PK properties while retaining efficacy and potency in the low nanomolar range at its target receptors. Furthermore, judicious structure-activity relationship (SAR) studies revealed key motifs that can be modulated to afford compounds with diverse selectivity profiles. Most importantly, we have demonstrated that select PACAP glycopeptide analogues (2LS80Mel and 2LS98Lac) exert potent neuroprotective effects and anti-inflammatory activity in animal models of traumatic brain injury and in a mild-toxin lesion model of Parkinson’s disease, highlighting glycosylation as a viable strategy for converting endogenous peptides into robust and efficacious drug candidates.
{"title":"Design and Synthesis of Brain Penetrant Glycopeptide Analogues of PACAP With Neuroprotective Potential for Traumatic Brain Injury and Parkinsonism","authors":"Christopher R. Apostol, K. Bernard, Parthasaradhireddy Tanguturi, G. Molnar, M. J. Bartlett, L. Szábo, Chenxi Liu, J. B. Ortiz, Maha Saber, K. Giordano, T. Green, James Melvin, Helena W. Morrison, L. Madhavan, R. Rowe, J. Streicher, M. Heien, T. Falk, R. Polt","doi":"10.3389/fddsv.2021.818003","DOIUrl":"https://doi.org/10.3389/fddsv.2021.818003","url":null,"abstract":"There is an unmet clinical need for curative therapies to treat neurodegenerative disorders. Most mainstay treatments currently on the market only alleviate specific symptoms and do not reverse disease progression. The Pituitary adenylate cyclase-activating polypeptide (PACAP), an endogenous neuropeptide hormone, has been extensively studied as a potential regenerative therapeutic. PACAP is widely distributed in the central nervous system (CNS) and exerts its neuroprotective and neurotrophic effects via the related Class B GPCRs PAC1, VPAC1, and VPAC2, at which the hormone shows roughly equal activity. Vasoactive intestinal peptide (VIP) also activates these receptors, and this close analogue of PACAP has also shown to promote neuronal survival in various animal models of acute and progressive neurodegenerative diseases. However, PACAP’s poor pharmacokinetic profile (non-linear PK/PD), and more importantly its limited blood-brain barrier (BBB) permeability has hampered development of this peptide as a therapeutic. We have demonstrated that glycosylation of PACAP and related peptides promotes penetration of the BBB and improves PK properties while retaining efficacy and potency in the low nanomolar range at its target receptors. Furthermore, judicious structure-activity relationship (SAR) studies revealed key motifs that can be modulated to afford compounds with diverse selectivity profiles. Most importantly, we have demonstrated that select PACAP glycopeptide analogues (2LS80Mel and 2LS98Lac) exert potent neuroprotective effects and anti-inflammatory activity in animal models of traumatic brain injury and in a mild-toxin lesion model of Parkinson’s disease, highlighting glycosylation as a viable strategy for converting endogenous peptides into robust and efficacious drug candidates.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47903086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.3389/fddsv.2022.1000827
Benjamin E Blass, Sumant Puri, Rishabh Sharma, Brian M Day
Invasive candidiasis remains a significant health concern, as it is associated with a high mortality risk. In addition, the risk of infection is significantly elevated in immunocompromised patients such as those with HIV, cancer, or those taking imcmunosuppressive drugs as a result of organ transplantation. The majority of these cases are caused by C. albicans, and C. glabrata is the second most common cause. These infections are typically treated using approved antifungal agents, but the rise of drug-resistant fungi is a serious concern. As part of our on-going effort to identify novel antifungal agents, we have studied the in vitro antifungal properties of a series of sulfonamide analogs of (2S, 4R)-Ketoconazole. Herein we report on the in vitro activity against the key fungal pathogens C. albicans, and C. glabrata.
{"title":"Antifungal properties of (2S, 4R)-Ketoconazole sulfonamide analogs.","authors":"Benjamin E Blass, Sumant Puri, Rishabh Sharma, Brian M Day","doi":"10.3389/fddsv.2022.1000827","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1000827","url":null,"abstract":"<p><p>Invasive candidiasis remains a significant health concern, as it is associated with a high mortality risk. In addition, the risk of infection is significantly elevated in immunocompromised patients such as those with HIV, cancer, or those taking imcmunosuppressive drugs as a result of organ transplantation. The majority of these cases are caused by <i>C. albicans</i>, and <i>C. glabrata</i> is the second most common cause. These infections are typically treated using approved antifungal agents, but the rise of drug-resistant fungi is a serious concern. As part of our on-going effort to identify novel antifungal agents, we have studied the <i>in vitro</i> antifungal properties of a series of sulfonamide analogs of (2S, 4R)-Ketoconazole. Herein we report on the <i>in vitro</i> activity against the key fungal pathogens <i>C. albicans</i>, and <i>C. glabrata</i>.</p>","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"2 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9514733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-11DOI: 10.3389/fddsv.2021.773424
N. Lamas, L. Roybon
Amyotrophic Lateral Sclerosis (ALS) is a motor neurodegenerative disorder whose cellular hallmarks are the progressive death of motor neurons (MNs) located in the anterior horn of the spinal cord, brainstem and motor cortex, and the formation of intracellular protein aggregates. Over the course of the disease, progressive paralysis takes place, leading to patient death within 3–5 years after the diagnosis. Despite decades of intensive research, only a few therapeutic options exist, with a limited benefit on the disease progression. Preclinical animal models have been very useful to decipher some aspects of the mechanisms underlying ALS. However, discoveries made using transgenic animal models have failed to translate into clinically meaningful therapeutic strategies. Thus, there is an urgent need to find solutions to discover drugs that could impact on the course of the disease, with the ultimate goal to extend the life of patients and improve their quality of life. Induced pluripotent stem cells (iPSCs), similarly to embryonic stem cells (ESCs), have the capacity to differentiate into all three embryonic germ layers, which offers the unprecedented opportunity to access patient-specific central nervous system cells in an inexhaustible manner. Human MNs generated from ALS patient iPSCs are an exciting tool for disease modelling and drug discovery projects, since they display ALS-specific phenotypes. Here, we attempted to review almost 2 decades of research in the field, first highlighting the steps required to efficiently generate MNs from human ESCs and iPSCs. Then, we address relevant ALS studies which employed human ESCs and iPSC-derived MNs that led to the identification of compounds currently being tested in clinical trials for ALS. Finally, we discuss the potential and caveats of using patient iPSC-derived MNs as a platform for drug screening, and anticipate ongoing and future challenges in ALS drug discovery.
{"title":"Harnessing the Potential of Human Pluripotent Stem Cell-Derived Motor Neurons for Drug Discovery in Amyotrophic Lateral Sclerosis: From the Clinic to the Laboratory and Back to the Patient","authors":"N. Lamas, L. Roybon","doi":"10.3389/fddsv.2021.773424","DOIUrl":"https://doi.org/10.3389/fddsv.2021.773424","url":null,"abstract":"Amyotrophic Lateral Sclerosis (ALS) is a motor neurodegenerative disorder whose cellular hallmarks are the progressive death of motor neurons (MNs) located in the anterior horn of the spinal cord, brainstem and motor cortex, and the formation of intracellular protein aggregates. Over the course of the disease, progressive paralysis takes place, leading to patient death within 3–5 years after the diagnosis. Despite decades of intensive research, only a few therapeutic options exist, with a limited benefit on the disease progression. Preclinical animal models have been very useful to decipher some aspects of the mechanisms underlying ALS. However, discoveries made using transgenic animal models have failed to translate into clinically meaningful therapeutic strategies. Thus, there is an urgent need to find solutions to discover drugs that could impact on the course of the disease, with the ultimate goal to extend the life of patients and improve their quality of life. Induced pluripotent stem cells (iPSCs), similarly to embryonic stem cells (ESCs), have the capacity to differentiate into all three embryonic germ layers, which offers the unprecedented opportunity to access patient-specific central nervous system cells in an inexhaustible manner. Human MNs generated from ALS patient iPSCs are an exciting tool for disease modelling and drug discovery projects, since they display ALS-specific phenotypes. Here, we attempted to review almost 2 decades of research in the field, first highlighting the steps required to efficiently generate MNs from human ESCs and iPSCs. Then, we address relevant ALS studies which employed human ESCs and iPSC-derived MNs that led to the identification of compounds currently being tested in clinical trials for ALS. Finally, we discuss the potential and caveats of using patient iPSC-derived MNs as a platform for drug screening, and anticipate ongoing and future challenges in ALS drug discovery.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44059252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.3389/fddsv.2021.728551
J. Medina‐Franco
Computer-aided drug discovery (CADD) has become an essential part of several projects in different settings and research environments. CADD has largely contributed to identifying and optimizing hit compounds leading them to advanced stages of the drug discovery pipeline or the market (PrietoMartínez et al., 2019). CADD includes several theoretical disciplines, including chemoinformatics, bioinformatics, molecular modeling, and data mining, among others (López-López et al., 2021). Artificial intelligence (AI) that has been used since the 60 s (Gasteiger, 2020) in drug discovery is regaining momentum, in particular with machine learning (ML) and deep learning (DL) (Bajorath, 2021; Bender and Cortés-Ciriano, 2021). In parallel to the continued contribution of CADD, several methodologies used in CADD have entered the hype cycle with waives of hope, inflated expectations, disappointments, and productive applications. The disillusionments are frequently driven by fashion, exacerbated misuse, and a lack of proper training to interpret the results (MedinaFranco et al., 2021). Examples are quantitative structure-activity relationship studies (QSAR). A few decades ago, there was a hype for QSAR studies; but uneducated use, bad practices, and poor reporting led to inflated expectations and disappointment (Johnson, 2008). As part of the hype, scientific journals containing the word “QSAR” in the title emerged, and years later, some journals were re-named. Molecular docking is another example of a method that is often misused, leading to false expectations and disappointments, not because the technique is not useful but because it is tried to be used for purposes that was not initially designed (e.g., correlation of docking cores with experimental binding affinities). At the time of writing this manuscript, there is a hype for AI, ML, DL; quoting Bajorath, an “AI ecstasy” (Bajorath, 2021). Despite the contributions of CADD in different stages of the drug discovery pipelines and technological advances, there are challenges that need to be addressed. Table 1 outlines the grand challenges that face drug discovery using in silicomethods and AI and are further commented on in this manuscript. The list of topics is not exhaustive; the selected challenges are based on the author’s opinion, and it is intended to be a reference for a continued update. Here, the challenges are organized into six sections. The first two are related to the chemical and biological relevant chemical spaces, respectively; that is, what spaces are being explored? Another section covers methodological challenges: how is being conducted the search for new and better drugs at the intersection of the relevant chemical and biological spaces? The next three sections present hurdles associated with communication and Human interaction in research teams, scientific dissemination, data sharing, and education, respectively. The last section contains the Conclusions.
计算机辅助药物发现（CADD）已成为不同环境和研究环境下几个项目的重要组成部分。CADD在很大程度上有助于识别和优化命中化合物，使其进入药物发现管道或市场的高级阶段（PrietoMartínez等人，2019）。CADD包括几个理论学科，包括化学信息学、生物信息学、分子建模和数据挖掘等（López-López et al.，2021）。自60年代以来一直在药物发现中使用的人工智能（AI）（Gasteiger，2020）正在恢复势头，特别是在机器学习（ML）和深度学习（DL）方面（Bajorath，2021；Bender和Cortés-Ciriano，2021）。在CADD持续贡献的同时，CADD中使用的几种方法也进入了炒作周期，放弃了希望、夸大了期望、失望和富有成效的应用。幻想破灭往往是由时尚、滥用加剧以及缺乏适当的培训来解释结果所驱动的（MedinaFranco等人，2021）。定量构效关系研究（QSAR）就是一个例子。几十年前，QSAR研究大肆宣传；但未经教育的使用、不良做法和糟糕的报告导致了期望值的膨胀和失望（Johnson，2008）。作为炒作的一部分，标题中包含“QSAR”一词的科学期刊出现了，几年后，一些期刊被重新命名。分子对接是另一个经常被滥用的方法，导致错误的期望和失望，不是因为该技术没有用处，而是因为它试图用于最初没有设计的目的（例如，对接核心与实验结合亲和力的相关性）。在写这篇手稿的时候，有一个关于AI、ML、DL的炒作；引用Bajorath的话，一种“人工智能的狂喜”（Bajorath，2021）。尽管CADD在药物发现管道和技术进步的不同阶段做出了贡献，但仍有一些挑战需要解决。表1概述了使用计算机方法和人工智能进行药物发现所面临的巨大挑战，并在本文中进行了进一步评论。专题清单并非详尽无遗；所选择的挑战是基于作者的意见，旨在作为持续更新的参考。在这里，挑战分为六个部分。前两个分别与化学和生物相关的化学空间有关；也就是说，正在探索哪些空间？另一节介绍了方法上的挑战：如何在相关化学和生物空间的交叉点上寻找新的更好的药物？接下来的三节分别介绍了研究团队、科学传播、数据共享和教育中与沟通和人际互动相关的障碍。最后一节包含结论。
{"title":"Grand Challenges of Computer-Aided Drug Design: The Road Ahead","authors":"J. Medina‐Franco","doi":"10.3389/fddsv.2021.728551","DOIUrl":"https://doi.org/10.3389/fddsv.2021.728551","url":null,"abstract":"Computer-aided drug discovery (CADD) has become an essential part of several projects in different settings and research environments. CADD has largely contributed to identifying and optimizing hit compounds leading them to advanced stages of the drug discovery pipeline or the market (PrietoMartínez et al., 2019). CADD includes several theoretical disciplines, including chemoinformatics, bioinformatics, molecular modeling, and data mining, among others (López-López et al., 2021). Artificial intelligence (AI) that has been used since the 60 s (Gasteiger, 2020) in drug discovery is regaining momentum, in particular with machine learning (ML) and deep learning (DL) (Bajorath, 2021; Bender and Cortés-Ciriano, 2021). In parallel to the continued contribution of CADD, several methodologies used in CADD have entered the hype cycle with waives of hope, inflated expectations, disappointments, and productive applications. The disillusionments are frequently driven by fashion, exacerbated misuse, and a lack of proper training to interpret the results (MedinaFranco et al., 2021). Examples are quantitative structure-activity relationship studies (QSAR). A few decades ago, there was a hype for QSAR studies; but uneducated use, bad practices, and poor reporting led to inflated expectations and disappointment (Johnson, 2008). As part of the hype, scientific journals containing the word “QSAR” in the title emerged, and years later, some journals were re-named. Molecular docking is another example of a method that is often misused, leading to false expectations and disappointments, not because the technique is not useful but because it is tried to be used for purposes that was not initially designed (e.g., correlation of docking cores with experimental binding affinities). At the time of writing this manuscript, there is a hype for AI, ML, DL; quoting Bajorath, an “AI ecstasy” (Bajorath, 2021). Despite the contributions of CADD in different stages of the drug discovery pipelines and technological advances, there are challenges that need to be addressed. Table 1 outlines the grand challenges that face drug discovery using in silicomethods and AI and are further commented on in this manuscript. The list of topics is not exhaustive; the selected challenges are based on the author’s opinion, and it is intended to be a reference for a continued update. Here, the challenges are organized into six sections. The first two are related to the chemical and biological relevant chemical spaces, respectively; that is, what spaces are being explored? Another section covers methodological challenges: how is being conducted the search for new and better drugs at the intersection of the relevant chemical and biological spaces? The next three sections present hurdles associated with communication and Human interaction in research teams, scientific dissemination, data sharing, and education, respectively. The last section contains the Conclusions.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46491690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.3389/fddsv.2021.728469
B. Villoutreix
The history of drug discovery and medicine is as ancient as humanity with, according to numerous historians, the first evaluation of the medicinal values of some herbs as early as a few thousand years B.C. in China and in India. Indeed, the practice of Ayurveda, traditional Chinese medicines and the evidence of medicinal practice in Egypt have been documented thousands of years ago. Then in Greece, Hippocrates started to transform medicine from art to science. Intellectual contributions of many great minds from different countries developed over two thousand years gradually established the foundation of scientific medicine. Modern drug discovery started to emerge by the end of the 20th century. Today, this immense field of investigation is characterized by highly complex, time consuming, expensive (yet profitable), often unsuccessful, multidisciplinary processes carried out by a myriad of local, national and international public and private organizations. These players may have divergent interests, and are sometimes driven by concerns other than patients’ health. Given the complexity of drug discovery and development as a field, any attempt to improve the odds of success requires humility, consideration of opposing views and escaping intellectual silos. Although history cannot always foretell the future, there are certainly some lessons to be learned from the past. Strong statements about exploring only some specific scientific areas, with most of the time substantial budget cuts to other disciplines or other departments, should be avoided. For instance, in 1999, it was suggested that the Human Genome Project would transform drug discovery by 2010 and that by 2020 significant improvements in patient care through tailored therapies (personalized medicine) would take place. While the human genome project might change everything in the future, translation to new drugs has been limited (Joyner and Paneth, 2019). The problem is obviously not the human project in itself but the fact that it was oversold, expectations were not realistic and the complexity of the human body in the health and disease states largely underestimated. Along the same line of reasoning but about technologies, J. Bezdek proposed in 1993 a curve showing how technologies tend to progress with time and in silico drug design strategies were analyzed with this approach (Van Drie, 2007). The “Bezdek phases” usually involve an overreaction to immature technology, a peak of hype, followed by despair because the results do not match the expectations. Eventually, after years of efforts, true user benefits are noticed. As today artificial intelligence is increasingly over-advertised in drug discovery and development, the Bezdek theory may apply, suggesting that a curious enthusiastic but cautious approach is advisable (Schneider et al., 2020). Related to this is the need of high quality data that are often missing or maintained confidential in health-related research (Scannell and Bosley,
药物发现和医学的历史与人类一样古老，据许多历史学家称，早在公元前几千年，中国和印度就首次对一些草药的药用价值进行了评估。事实上，阿育吠陀的实践、传统中药和埃及医学实践的证据早在数千年前就有记载。然后在希腊，希波克拉底开始将医学从艺术转变为科学。两千多年来，各国杰出人才的智力贡献逐渐奠定了科学医学的基础。现代药物的发现始于20世纪末。今天，这一庞大的调查领域的特点是由无数地方、国家和国际公共和私人组织进行的高度复杂、耗时、昂贵（但有利可图）、往往不成功的多学科过程。这些参与者可能有不同的兴趣，有时是出于患者健康以外的考虑。鉴于药物发现和开发作为一个领域的复杂性，任何提高成功几率的尝试都需要谦逊，考虑对立的观点，并摆脱知识孤岛。尽管历史不能总是预言未来，但肯定有一些教训可以从过去吸取。应该避免关于只探索某些特定科学领域的强烈声明，而在大多数情况下，其他学科或其他部门的预算会大幅削减。例如，1999年，有人建议人类基因组计划将在2010年前改变药物发现，到2020年，通过量身定制的疗法（个性化药物）将显著改善患者护理。虽然人类基因组计划可能会改变未来的一切，但向新药的转化是有限的（Joyner和Paneth，2019）。问题显然不在于人类项目本身，而在于它被高估了，人们的期望不现实，人体在健康和疾病状态下的复杂性在很大程度上被低估了。J.Bezdek在1993年提出了一条曲线，显示了技术如何随着时间的推移而发展，并用这种方法分析了计算机药物设计策略（Van Drie，2007）。“Bezdek阶段”通常涉及对不成熟技术的过度反应、炒作的高峰，然后是绝望，因为结果与预期不符。最终，经过多年的努力，真正的用户利益得到了关注。随着今天人工智能在药物发现和开发中越来越被过度宣传，Bezdek理论可能适用，这表明一种好奇、热情但谨慎的方法是可取的（Schneider等人，2020）。与此相关的是对高质量数据的需求，这些数据在健康相关研究中经常缺失或保密（Scannell和Bosley，2016；Bender和Cortés-Ciriano，2021）。如今，虽然时尚的研究主题似乎不可避免，但决策者、科学家和患者应该记住，炒作周期可能会变成炒作泡沫，这种周期可能对科学有害（Rinaldi，2012）。在药物发现和开发领域，似乎有几个不同的挑战在等待我们，新冠肺炎的急剧流行可能是加速变化的催化剂之一（Aghila Rani et al.，2021）。在众多挑战中，以下简要讨论了一些挑战。编辑和审查：墨西哥国立自治大学JoséL Medina Franco
{"title":"Post-Pandemic Drug Discovery and Development: Facing Present and Future Challenges","authors":"B. Villoutreix","doi":"10.3389/fddsv.2021.728469","DOIUrl":"https://doi.org/10.3389/fddsv.2021.728469","url":null,"abstract":"The history of drug discovery and medicine is as ancient as humanity with, according to numerous historians, the first evaluation of the medicinal values of some herbs as early as a few thousand years B.C. in China and in India. Indeed, the practice of Ayurveda, traditional Chinese medicines and the evidence of medicinal practice in Egypt have been documented thousands of years ago. Then in Greece, Hippocrates started to transform medicine from art to science. Intellectual contributions of many great minds from different countries developed over two thousand years gradually established the foundation of scientific medicine. Modern drug discovery started to emerge by the end of the 20th century. Today, this immense field of investigation is characterized by highly complex, time consuming, expensive (yet profitable), often unsuccessful, multidisciplinary processes carried out by a myriad of local, national and international public and private organizations. These players may have divergent interests, and are sometimes driven by concerns other than patients’ health. Given the complexity of drug discovery and development as a field, any attempt to improve the odds of success requires humility, consideration of opposing views and escaping intellectual silos. Although history cannot always foretell the future, there are certainly some lessons to be learned from the past. Strong statements about exploring only some specific scientific areas, with most of the time substantial budget cuts to other disciplines or other departments, should be avoided. For instance, in 1999, it was suggested that the Human Genome Project would transform drug discovery by 2010 and that by 2020 significant improvements in patient care through tailored therapies (personalized medicine) would take place. While the human genome project might change everything in the future, translation to new drugs has been limited (Joyner and Paneth, 2019). The problem is obviously not the human project in itself but the fact that it was oversold, expectations were not realistic and the complexity of the human body in the health and disease states largely underestimated. Along the same line of reasoning but about technologies, J. Bezdek proposed in 1993 a curve showing how technologies tend to progress with time and in silico drug design strategies were analyzed with this approach (Van Drie, 2007). The “Bezdek phases” usually involve an overreaction to immature technology, a peak of hype, followed by despair because the results do not match the expectations. Eventually, after years of efforts, true user benefits are noticed. As today artificial intelligence is increasingly over-advertised in drug discovery and development, the Bezdek theory may apply, suggesting that a curious enthusiastic but cautious approach is advisable (Schneider et al., 2020). Related to this is the need of high quality data that are often missing or maintained confidential in health-related research (Scannell and Bosley, ","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44528599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：